جست‌وجوی استاد و پژوهشگر

Mark Schmidtمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Mathematical Optimization
• +۶ مورد دیگر

Mark Schmidt is a Professor in the Department of Computer Science at the University of British Columbia, Faculty of Science. He holds the Canada Research Chair in Large-Scale Machine Learning and is a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute. His research spans multiple centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action), the Data Science Institute, and the Machine Intelligence Learning Discovery (MILD) group. Dr. Schmidt's educational background includes a Ph.D. from UBC (2005-2010), an M.Sc. from the University of Alberta (2003-2005), and a B.Sc. from the University of Alberta (2000-2003). His academic career progressed from Postdoc positions at Simon Fraser University, Ecole Normale Superieure, and UBC to Assistant Professor (2014-2019), Associate Professor (2019-2024), and current Professor (2024-present) at UBC. His research focuses on machine learning optimization, with particular emphasis on improving numerical algorithms for large-scale machine learning applications. His work bridges theoretical optimization and practical applications across computer vision, natural language processing, and reinforcement learning. He has developed numerous optimization techniques including variants of stochastic gradient methods, coordinate descent algorithms, and natural gradient approaches. Analysis of his recent publications reveals a strong focus on optimization for over-parameterized models, particularly transformers and large language models. His work addresses critical challenges in step-size selection, convergence guarantees, and efficient implementation of optimization algorithms. His research has significant implications for training deep neural networks more effectively and understanding why certain optimization methods outperform others in practice. Among his notable awards are the Dorothy Killam Fellowship (2025), Arthur B. McDonald Fellowship (2024), Sloan Research Fellowship (2017), and multiple UBC teaching awards. He has also received the Lagrange Prize in Continuous Optimization and Best Paper Award at AISTATS 2021. Dr. Schmidt actively supervises numerous graduate students, with over 40 PhD and Master's students listed as current or alumni members of his research group. His laboratory maintains strong connections with industry partners, with many alumni securing positions at leading AI companies including Google, Amazon, Meta, and Microsoft.

Marlyse Baptistaمشاهده پروفایل

استاد

• Language Contact
• Cognitive Linguistics
• Pidgins and Creoles
• +۴ مورد دیگر

Marlyse Baptista is the President's Distinguished Professor of Linguistics at the University of Pennsylvania, Department of Linguistics. She is affiliated with the School of Arts & Sciences and MindCore initiative. Her research focuses on language contact, creolization processes, bilingualism, and experimental methods in creole studies. Baptista leads the Language Contact and Cognition Lab, previously known as the Cognition, Convergence and Language Emergence (CCLE) group at the University of Michigan. Education includes a PhD in Linguistics (Harvard, 1997), MA degrees from Harvard and the Université de Bordeaux III, and extensive training in multilingual education. Her work bridges generative syntax with experimental approaches, including artificial language learning to study language convergence. Key themes include the cognitive underpinnings of creole formation, bidirectional influences in Cape Verdean Creole, and the role of congruence in language acquisition. Research highlights include studies on Cape Verdean Creole's grammatical properties, genetic-linguistic admixture correlations, and pedagogical resources for creole language education. Recent projects explore experimental validation of creole genesis theories through multilingual acquisition studies. Scientific Awards: President's Distinguished Professorship (University of Pennsylvania) Labs/Teams: Language Contact and Cognition Lab, MindCore Grants/Projects: MULTI Project (creole language educational resources), NSF-funded studies on creole genesis mechanisms Baptista's work emphasizes interdisciplinary approaches, integrating syntax theory, psycholinguistics, and sociolinguistics to address foundational questions in contact linguistics and creolistics.

Shivaram Kalyanakrishnanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Shivaram Kalyanakrishnan is an Associate Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay , specialising in Artificial Intelligence and Machine Learning . His research spans sequential decision making , multiagent learning , multi-armed bandits , and humanoid robotics , with applications in robot soccer , computer games , and online advertising . He teaches advanced courses like CS 747: Foundations of Intelligent and Learning Agents and CS 748: Advances in Intelligent and Learning Agents , focusing on end-to-end system design and theoretical analysis. His scientific awards include the Best Student Paper Award at RoboCup International Symposium 2006 and nomination for Best Student Paper Award at AAMAS 2007 . His work on reinforcement learning and policy iteration has been published in leading venues such as IJCAI , ICML , and COLT , with recent contributions to railway scheduling and bandit algorithms. While no explicit list of advisees is provided, his research projects and publications suggest mentorship of students in collaborative efforts. Contact : shivaram@cse.iitb.ac.in .

• Machine Learning
• Probability
• Mathematical Physics
• +۶ مورد دیگر

Boris Hanin is an Associate Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE) and Associated Faculty at the Program in Applied and Computational Mathematics (PACM). Prior to Princeton, he held academic positions at Texas A&M University (Assistant Professor of Mathematics), MIT (NSF Postdoctoral Fellow), and Northwestern University (PhD in Mathematics under Steve Zelditch). He works part-time at Foundry, an AI/computing startup, leading the Foundry Institute. PhD in Mathematics, Northwestern University NSF Postdoctoral Fellowship, MIT Mathematics Associate Professor, Princeton ORFE Part-Time Leader, Foundry Institute His research spans machine learning, probability theory, and mathematical physics, focusing on neural network theory (approximation power, optimization guarantees), random matrix theory, and spectral asymptotics. He has made foundational contributions to understanding gradient behavior, initialization, and infinite-width limits in deep learning. Boris has supervised numerous PhD students and postdocs, with former members securing prestigious positions at Harvard, MIT, and Huawei. He serves as Associate Editor for journals like Pure and Applied Analysis and Mathematics of Operations Research , and has taught short courses at Oxford, Luxembourg, and Tor Vergata on statistical physics of neural networks and deep learning theory. 2024 Sloan Fellowship in Mathematics NSF CAREER grant DMS-2143754 NSF grant DMS-2133806 His research group collaborates on topics including hyperparameter transfer, architecture-aware scaling, and spectral analysis of random waves and neural networks. He actively contributes to theoretical machine learning through foundational publications in venues like Probability Theory and Related Fields , Journal of Machine Learning Research , and Communications in Mathematical Physics .

Kevin Jamiesonمشاهده پروفایل

دانشیار

• Machine Learning
• Active Learning
• Multi-Armed Bandits
• +۴ مورد دیگر

Kevin Jamieson is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering and an Adjunct Professor in the Department of Statistics at the University of Washington . His academic journey includes a B.S. (2009) , M.S. (2010) , and Ph.D. (2015) in electrical engineering from the University of Washington, Columbia University, and University of Wisconsin–Madison respectively. He completed a postdoc at UC Berkeley's AMP Lab before joining UW in 2017. Ph.D., Electrical Engineering, University of Wisconsin–Madison (2015) M.S., Electrical Engineering, Columbia University (2010) B.S., Electrical Engineering, University of Washington (2009) Jamieson's research lies at the intersection of interactive machine learning , active learning , and sequential decision making . His work focuses on: Adaptive sampling strategies in multi-armed bandits and reinforcement learning (RL) Developing instance-dependent optimal algorithms that adapt to problem difficulty Applications in robotics , human perception studies , and hyperparameter optimization Representation learning for large models and experimental design frameworks His 15 most recent publications (2025-2022) demonstrate expertise in bandit theory , contextual RL , and game-theoretic learning . Notable trends include sample-efficient optimization , adaptive A/B testing , and sim-to-real transfer in robotics. Jamieson has received: NSF CAREER award for foundational contributions Amazon Faculty Research award for innovation in learning systems He actively recruits graduate students and postdocs , emphasizing collaboration in areas like: Multi-agent RL and strategic actor learning Empirical process suprema and adaptive sampling theory Applications in robotics , large language model finetuning , and biomedical data analysis Jamieson leads the Washington AI Lab (WAIL) and develops open-source learning systems like the NEXT framework for real-world adaptive data collection. He serves as co-PI for the Institute for the Foundations of Data Science (IFDS) and co-organizes the Distinguished Seminar in Optimization & Data .

Yee Whye Tehمشاهده پروفایل

استاد

• Statistical Machine Learning
• Probabilistic Learning
• Deep Learning
• +۴ مورد دیگر

Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.

Jason D. Leeمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Jason D. Lee is an associate professor of Electrical Engineering and Computer Sciences (EECS) and Statistics at the University of California, Berkeley. Previously, he held academic positions at Princeton University as an associate professor, and was a research scientist at Google DeepMind. He completed his Ph.D. in Computational and Mathematical Engineering at Stanford University under the advisement of Trevor Hastie and Jonathan Taylor. For students and collaborators, his primary contact email is jasonlee@princeton.edu, though prospective students and postdocs are asked to include "filter_student" in the subject line. Ph.D., Computational and Mathematical Engineering, Stanford University (2015) B.Sc., Mathematics, Duke University (2010) Lee's research lies at the intersection of machine learning, statistics, and optimization, focusing on the theoretical foundations of artificial intelligence. His work addresses fundamental questions in deep learning, including optimization landscapes, representation learning, and reinforcement learning theory. He has made significant contributions to understanding how gradient descent operates in neural network training and has developed provably efficient algorithms for various learning scenarios. His ten most recent publications represent a diverse yet coherent body of work across machine learning theory, focusing on topics such as Gaussian multi-index models, transformer learning capabilities, shallow neural networks, and optimization techniques. These publications appear in top venues including COLT, ICML, NeurIPS, and JMLR. Among his notable accolades are: Samsung AI Researcher of the Year Award (2023) NSF Career Award (2022) ONR Young Investigator Award (2021) Sloan Research Fellow in Computer Science (2019) NIPS Best Student Paper Award (2016) Princeton Commendation for Outstanding Teaching (ECE538B) Lee has advised numerous students including Alex Damian, Wenhao Zhan, Eshaan Nichani, Tianle Cai, Zixuan Wang, and Yunwei Ren. Former advisees have gone on to positions at institutions like NYU Courant, Facebook AI Research, UW, MIT, Duke, and Microsoft Research. His research group and collaborators span multiple institutions, working on theoretical and applied aspects of machine learning and artificial intelligence, with a particular focus on the optimization and learning dynamics of neural networks and transformer models.

Xin Guoمشاهده پروفایل

استاد

• Mathematical Finance
• Financial Engineering
• FinTech
• +۷ مورد دیگر

Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.

Aarti Singhمشاهده پروفایل

استاد

• Reinforcement Learning
• Bandit Algorithms
• Active Learning
• +۷ مورد دیگر

Aarti Singh is a Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF AI Institute for Societal Decision Making. She leads research at the intersection of machine learning, statistics, and decision making, with applications to scientific and societal domains. Her work focuses on designing principled interactive algorithms for learning and decision making under uncertainty. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2008) M.S. in Electrical Engineering, University of Wisconsin-Madison (2003) B.E. in Electronics and Communication Engineering, University of Delhi (2001) Research Interests: Professor Singh's research centers on developing interactive machine learning algorithms that go beyond finding input-output associations to make higher-level decisions about the most informative data and actions. Her work spans autonomous decision making, including active sampling, stochastic optimization, bandits, and reinforcement learning that are statistically optimal, computationally tractable, and robust. She also investigates human factors in decision making, designing algorithms that model and leverage human feedback while accounting for bias, memory effects, and calibration. Her research has applications in material science, cosmology, and peer review systems. Research Trends: Professor Singh's recent publications demonstrate a strong focus on reinforcement learning, particularly in developing more efficient and robust algorithms for decision making under uncertainty. Her work bridges theoretical foundations with practical applications, spanning from fundamental algorithm development to real-world implementation in scientific domains. There's a clear trajectory toward integrating human factors into decision-making algorithms, with significant contributions to peer review systems and preference learning. Scientific Awards: NSF Career Award United States Air Force Young Investigator Award A. Nico Habermann Faculty Chair Award Harold A. Peterson Best Dissertation Award Multiple paper awards Advising and Grants: Professor Singh has advised numerous PhD and master's students, many of whom have gone on to faculty positions or research roles at leading institutions. Her research is supported by prestigious grants from ONR, Simons Foundation, AFRL, ARL, and NSF. She serves as General Chair (2025) and Program Chair (2020) for the International Conference on Machine Learning (ICML) and has held leadership roles in multiple professional organizations. Research Team: Professor Singh leads a vibrant research group within the Machine Learning Department at CMU, with current PhD students working on topics including reinforcement learning, human-AI collaboration, and decision making under uncertainty. She also directs the NSF AI Institute for Societal Decision Making, which brings together researchers from multiple disciplines to develop AI systems that support human decision making in societal contexts.

Eric Eatonمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Artificial Intelligence
• Lifelong Learning
• +۵ مورد دیگر

Eric Eaton is a Research Associate Professor of Computer and Information Science at the University of Pennsylvania , and a core member of the GRASP (General Robotics, Automation, Sensing, and Perception) Laboratory . His expertise centers on lifelong machine learning , transfer learning , and interactive AI , with impactful applications to robotics, precision medicine, and computational sustainability. Education & Affiliations Ph.D. in Computer Science, University of Maryland, Baltimore County (UMBC) – dissertation on selective knowledge transfer advised by Marie desJardins Former Visiting Assistant Professor, Bryn Mawr College Former part-time faculty, Swarthmore College and UMBC Two years as Senior Research Scientist at Lockheed Martin Advanced Technology Laboratories Research Interests Eaton’s research advances versatile AI systems that can learn multiple tasks over long lifetimes, transfer knowledge across domains, and interact effectively with humans and other agents. Core themes include: Lifelong & continual learning – continual acquisition and refinement of knowledge across tasks Knowledge transfer – selective, cross-domain, and zero-shot transfer techniques Interactive AI – human-in-the-loop learning and interpretable models Applications – autonomous service robotics, precision medicine, sustainability, and search & rescue Selected Scientific Awards IJCAI-16 Distinguished Student Paper Award for zero-shot transfer research IJCAI-15 Best Paper Nomination for autonomous cross-domain transfer ICML 2020 Workshop Best Paper Award for lifelong policy gradient learning Grants & Funding Current and past research is supported by the Office of Naval Research (ONR), the National Science Foundation (NSF), and Lockheed Martin, enabling large-scale projects in lifelong robotics and medical AI. Students & Postdocs Eaton has advised a diverse cohort of scholars including current PhD students David Isele, Seungwon Lee, Jorge Mendez, and Mohammad Rostami, as well as postdocs Boyu Wang and numerous alumni now in faculty positions or leading industry research teams. Labs & Teams He leads the Autonomous Service Robot Fleet within GRASP, creating low-cost robots that learn lifelong skills in university and office settings. His group also collaborates with clinicians for AI-driven precision medicine, and partners with defense and sustainability initiatives.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۸ مورد دیگر

Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines

Maria Gouskovaمشاهده پروفایل

استاد

• Phonology
• Morphology
• Lexical Phonology
• +۳ مورد دیگر

Maria Gouskova is a Professor of Linguistics at the Department of Linguistics, New York University (NYU). She is affiliated with the College of Arts and Science and holds editorial roles as an Associate Editor of Language and board member of NLLT and Phonology . Her research focuses on phonology, morphology, and lexicon, with a particular emphasis on morphophonological interactions, sublexicons, and phonotactic constraints. She earned her Ph.D. in Linguistics from the University of Massachusetts, Amherst (2003) and a B.A. in English Linguistics and German Language/Literature from Eastern Michigan University (1998). Her work bridges theoretical phonology and experimental methods, addressing questions such as how phonological patterns interact with morphology, the role of sublexical phonotactics in grammatical processes, and the learnability of complex segmental inventories. Recent research includes studies on Russian diminutive affixes, gradient phonological constraints, and the phonological properties of compounds. Her publications span topics like allomorphy, lexical phonology, and the typology of morpheme structure constraints. She frequently collaborates on projects investigating the interplay between syntax, phonology, and morphology, as seen in studies of Russian prepositions and compound stress patterns. Her contributions to phonological theory include advancing models of sublexicon theory and nonlocal constraint induction.

Sicun Gaoمشاهده پروفایل

دانشیار

• Algorithms and Optimization
• Automated Reasoning
• Cyber-Physical Systems
• +۳ مورد دیگر

Sicun Gao is an Associate Professor in the Computer Science and Engineering department at the University of California, San Diego. His research focuses on practical algorithms for NP-hard search and optimization problems in computational systems, emphasizing combinatorial perspectives in numerical and statistical contexts to achieve reliable autonomy. Research Interests: Automated reasoning, Hamilton-Jacobi reachability, safe reinforcement learning, control barrier functions, and optimization in cyber-physical systems. Teaching: Courses on AI search, optimization, and graduate research seminars. The 15 most recent publications highlight advancements in safe AI control, motion planning, and policy optimization, often integrating neural networks with formal verification. Awards include the IEEE Power & Energy Society Technical Committee Prize Paper Award and the IROS RoboCup Best Paper Award. He advises PhD students working on AI-driven control and robotics, with alumni placed at institutions like Seoul National University, Amazon, and Apple. Grants include NSF Career, Air Force Young Investigator, and DARPA Assured Autonomy funding. His lab develops tools like dReal for automated reasoning in nonlinear theories over the reals.

Edward S. Awhمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Visual Working Memory
• Selective Attention
• +۴ مورد دیگر

Edward S. Awh is a Professor in the Department of Psychology at the University of Chicago, affiliated with The Institute for Mind and Biology and the Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior. His research focuses on behavioral and neural studies of memory and attention, employing psychophysics, EEG, and functional MRI. His lab investigates neural mechanisms underlying cognitive processes and their interrelationships. Research interests include tracking the contents of online memories and the locus of covert attention using neural decoding techniques. The lab collaborates on projects involving attention and memory studies, as detailed on their website: Awh/Vogel Lab . The lab currently seeks postdoctoral researchers to join ongoing projects using behavioral, EEG, and fMRI methodologies. Contact via awhvogellab@gmail.com .

Roger Giner-Sorollaمشاهده پروفایل

استاد

• Moral Psychology
• Social Psychology
• Emotion Research
• +۷ مورد دیگر

Roger Giner-Sorolla is Professor of Social Psychology at the University of Kent's School of Psychology, where he has been a faculty member since 2001 after completing his PhD at New York University in 1996. He served as Editor-in-Chief of the Journal of Experimental Social Psychology from 2016-2022 and currently leads a Leverhulme Foundation grant examining moral heroism across professions. His academic journey includes a postdoctoral fellowship at the University of Virginia and has established him as a leading figure in social and moral psychology. Professor Giner-Sorolla's research focuses on the social role of emotions, particularly moral emotions including self-condemning emotions (guilt and shame) and other-condemning emotions (anger, contempt, and disgust). His work extends to intergroup apologies, dehumanization, emotionally driven prejudice, and collective moral roles, with a notable side interest in the ironic enjoyment of music and aesthetic experiences. He is a strong advocate for transparency in scientific reporting, having written extensively on improved reporting guidelines, pre-registration, and open science practices. Analysis of his recent publications reveals a consistent focus on moral psychology with expanding interdisciplinary reach. His work bridges traditional social psychology with emerging areas including cross-cultural emotion research, replication science, and historical memory. The trend shows increasing attention to methodological rigor while maintaining core interests in emotion and morality, with growing emphasis on practical applications in conflict resolution and intergroup relations. Fellow of the Association for Psychological Science Member of Rising Stars selection committee (2023-2024) Promising Scholars Award (University of Kent, 2006) Vacation Research Scholarship (Wellcome Trust, 2005) Multiple ESRC grants totaling over £400,000 European Science Foundation Workshop Award (2004) Professor Giner-Sorolla has supervised an impressive number of PhD students, with 17 doctoral candidates listed in the documentation including both current and past students. His grant portfolio demonstrates significant research impact with funding from major organizations including ESRC, Wellcome Trust, and Leverhulme Foundation. His supervision style appears to encourage students to pursue research that aligns with his core interests in moral emotions while allowing exploration of novel applications and contexts. While the documentation doesn't specify laboratory or research team structures, his extensive collaborative work suggests leadership of research groups focused on moral psychology and emotion research. His numerous international collaborations and invited workshops indicate an active research program with global reach, likely involving multiple research teams working on interconnected projects related to his core interests in moral psychology and scientific methodology.